A Framework for Testing Identifiability of Bayesian Models of Perception

Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Bayesian observer models are very effective in describing human performance in perceptual tasks, so much so that they are trusted to faithfully recover hidden mental representations of priors, likelihoods, or loss functions from the data. However, the intrinsic degeneracy of the Bayesian framework, as multiple combinations of elements can yield empirically indistinguishable results, prompts the question of model identifiability. We propose a novel framework for a systematic testing of the identifiability of a significant class of Bayesian observer models, with practical applications for improving experimental design. We examine the theoretical identifiability of the inferred internal representations in two case studies. First, we show which experimental designs work better to remove the underlying degeneracy in a time interval estimation task. Second, we find that the reconstructed representations in a speed perception task under a slow-speed prior are fairly robust.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 27
EditorsZ. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger
PublisherCurran Associates Inc
Number of pages9
Publication statusPublished - 2014


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